#1 Processing steps

  1. Perform the simulation

For each flight simulation, “Flightapp” generates 3 tables:
  • flight (position, speed, etc.)
  • actions (control actions performed by the pilot)
  • imquery (information checks performed by the pilot)

  1. Discretize simulation tables

  • For each flight, “PostFlight” generates 1 table (ending with “dis”), and we export as CSV for processing with R.
  • All CSV flights need to be stored in a specific folder of the R path (“csv_in/”).

  1. Create “big” dataset in R

Using the function “createInfoDF”, that returns a list of dataframes, we obtain the dataset with all flights and the initial variables. Useless rows are discarded in this step.

  1. The “flights_info” table

We also obtain a dataframe with a summary of the dataset, we call it “flights_info”:
id disTable initialRows firstIndex lastIndex deviationfirstIndex deviationlastIndex deviationexceeded deviationmax deviationmean
1 20220121223209dis 1977 1 1930 0.0004094 0.3090253 0.0000000 0.9809517 0.3187659
2 20220121230742dis 1949 1 1917 0.0002194 0.0529804 0.8868023 1.0245125 0.2287594
3 20220123194045dis 1996 1 1917 0.0004871 0.2986273 0.0000000 0.6667019 0.1708445
4 20220125113713dis 1962 1 1919 0.0005534 0.1627686 0.0000000 0.6390038 0.1720996
5 20220316145030dis 1943 1 1845 0.0003181 0.0552582 3.1978320 1.1774307 0.2293945
6 20220316153735dis 1900 1 1871 0.0003222 0.0161252 0.0000000 0.7176776 0.1638964
7 20220512161006dis 1962 1 1920 0.0005859 0.3391114 0.0000000 0.8794430 0.2712950
8 20220523212158dis 1911 1 1890 0.0003100 0.0162455 0.0000000 0.8161311 0.1295424
9 20220524203226dis 1938 1 1904 0.0003080 0.0277673 0.0000000 0.7762082 0.1418865
10 20220527110631dis 1934 1 1896 0.0004087 0.1945740 0.0000000 0.5697111 0.1893888
11 20220528112722dis 1917 1 1888 0.0004087 0.0203251 1.9597458 1.0638933 0.1926771
12 20220529142028dis 1905 1 1877 0.0003161 0.0143047 0.0000000 0.7765556 0.1639485
13 20220530144122dis 1880 1 1860 0.0004797 0.0182252 0.0000000 0.8486337 0.1627407
14 20220531151247dis 1907 1 1884 0.0003127 0.0318930 0.0000000 0.8364559 0.1990694
15 20220601154130dis 1916 1 1889 0.0002255 0.1581891 0.0000000 0.5939349 0.1251029
16 20220602160225dis 1948 1 1887 0.0003107 0.0383346 0.0000000 0.5766042 0.1002377
17 20220603192433dis 1914 1 1877 0.0004804 0.0215958 0.0000000 0.5849015 0.1181386
18 20220604200536dis 1923 1 1893 0.0003925 0.1224819 0.0000000 0.4652699 0.0828084
19 20220906134140dis 1914 1 1882 0.0003134 0.0731759 0.0000000 0.9665278 0.1924313
20 20220906153443dis 1894 1 1872 0.0004932 0.1074852 0.0000000 0.8681653 0.1823608
21 20220906200133dis 1916 1 1874 0.0003201 0.1345631 0.0000000 0.9313942 0.1819750
22 20220907072201dis 1927 1 1898 0.0002248 0.0846236 0.0000000 0.5042391 0.1283485
23 20230617165137dis 1917 1 1886 0.0108562 0.0359365 17.9215270 2.0249735 0.5695217
24 20230909190522dis 1862 1 1845 0.0003634 0.0701776 8.1300813 2.0921676 0.4119222
25 20230909184627dis 1856 1 1815 0.0005068 0.0271638 0.0000000 0.7527038 0.1525801
26 20240208215046dis 1878 1 1846 0.0005169 0.1112870 0.0000000 0.7122132 0.1827100
27 20240208211725dis 1870 1 1812 0.0002640 0.2999301 0.0000000 0.6387079 0.1753212
  • For each one of the 27 flights, we see the total number of rows and the rows of interest (initialRows, firstIndex, lastIndex), which are kept.
  • We also obtain basic statistics of the deviation:
  • deviationfirstIndex & deviationlastIndex provide an indication that the flight is acceptable at the beginning and the end.
  • deviationexceeded is the percentage of samples where the deviation is bigger than 1NM.
  • deviationmax is the maximum deviation within the flight, and deviationmean is the mean.

  1. Overview of the flights

This is a map representation of each flight:
Flight 1 :
Flight 2 :
Flight 3 :
Flight 4 :
Flight 5 :
Flight 6 :
Flight 7 :
Flight 8 :
Flight 9 :
Flight 10 :
Flight 11 :
Flight 12 :
Flight 13 :
Flight 14 :
Flight 15 :
Flight 16 :
Flight 17 :
Flight 18 :
Flight 19 :
Flight 20 :
Flight 21 :
Flight 22 :
Flight 23 :
Flight 24 :
Flight 25 :
Flight 26 :
Flight 27 :